1. Field of the Invention
This invention relates to data communication systems and methods. More particularly, this invention relates to a system and method for timing recovery.
2. Description of the Background Art
Communication systems increasingly depend on digital data transmission. Digital data transmission, in turn, depends on reliable reception of transmitted data. Effective timing recovery facilitates reliable reception of transmitted data in a digital data receiver. More specifically, effective timing recovery facilitates correct sampling instances of the received data stream. In other words, certain digital data receivers continuously adjust the frequency and phase of the receiver clock to optimize the sampling instants of the received data signal and to compensate for frequency drifts between the oscillators used in the transmitter and receiver clock circuits.
A receiver can derive the timing information from the data signal itself. There are at least three timing recovery methods that recover timing information from the data signal. A first method detects the zero-crossing points of sampled data. See B. R. Saltzberg, “Timing recovery for synchronous binary data transmission,” Bell System Technical Journal, vol.46, pp.593-622, March 1967, which is incorporated herein by reference in its entirety. A receiver determines the sampling points as the mid-point between two adjacent crossings. This mid-point likely coincides with the maximum eye opening in an eye diagram. As is well known in the art, a display, e.g., an oscilloscope, connected to a demodulated, filtered symbol stream, can generate an eye diagram. The display retriggers at every symbol period or fixed multiple of the symbol period using a symbol timing signal derived from the received waveform to produce the eye diagram.
A second method exploits the signal derivatives at the sampling instants. See H. Kobayashi, “Simultaneous adaptive estimation and decision algorithm for carrier modulated data transmission systems,” IEEE Trans. Communications, vol. COM-19, pp. 268-280, June 1971; R. D. Gitlin and J. Salz, “Timing recovery in PAM systems,” Bell System Technical Journal, vol. 50, pp. 1645-1669, May-June 1971, which are both incorporated herein by reference in their entirety. This method adjusts the sampling phase until the signal derivative at the sampling instant is zero, at which point, the method samples the data symbols at their peaks.
A third method involves applying a non-linear operation, such as squaring, to the received filtered data stream. The non-linear operation generates a signal with a strong, discrete frequency component, e.g., a spectral line, at the symbol timing frequency. A subsequent filtering operation with a sharp bandpass filter extracts the frequency of the symbol clock. See W. R. Bennett, “Statistics of regenerative digital transmission,” Bell System Technical Journal, vol.37, pp.1501-1542, November 1958; Y Takasaki, “Timing extraction in baseband pulse transmission,” IEEE Trans. Communications, vol. COM-20, pp. 877-884, October 1972; L. E. Franks and J. P. Bubrouski, “Statistical properties of timing jitter in a PAM timing recovery system,” IEEE Trans. Communications, vol. COM-22, pp.913-920, July 1974, which are all incorporated herein by reference in their entirety.
Many baud timing recovery systems use only one sample per baud interval, i.e., they use baud sampling. The information used by the timing recovery methods described above is not available with baud sampling. For example, with respect to the first method described above, a receiver performing baud sampling does not detect signal crossings with any useful precision. Unfortunately, the use of higher sampling rates or additional sampling of the signal derivative for timing recovery is not an appealing solution because of the corresponding increase in expense, complexity, and amount of hardware.
Kurt H. Muller and Markus Muller introduced a baud-rate timing recovery scheme which exploits a timing function based on sampled data and estimated data values. The output of the timing function determines the sampling instants. See “Timing recovery in digital synchronous data receivers,” IEEE Trans. Communications, vol. COM-24, no.5, pp. 516-531, May 1976 which is incorporated herein by reference in its entirety. The success of the scheme depends on how accurately it can estimate the received data. Hence, when using a channel that severely distorts the transmitted signal, the Muller and Muller scheme can fail to operate properly without a training sequence.
When using a channel that severely distorts the transmitted signal, an automatic equalizer is useful to compensate for the distortion. However, if the system does not incorporate a training sequence, the automatic equalizer will not accurately estimate the incoming data values until the equalizer settles. As noted above, the timing function is a function of the estimated data values and the timing function determines the timing of the sampling instants. Thus, the timing of the sampling instants drifts until the equalizer settles if the timing recovery system uses a timing function that is a function of the sampled data values and the estimated data values. Furthermore, when the timing of the sampling instants drifts the equalizer typically does not achieve stable operation. Consequently, joint operation of the equalizer and the timing recovery system is needed.
U.S. Pat. No. 3,697,689 to E. D. Gibson, entitled “Fine timing recovery system,” which is incorporated herein by reference in its entirety, describes one such method, i.e., a method that provides joint operation of the equalization and the timing recovery. The Gibson patent describes using tap coefficients of a linear zero-forcing equalizer with a transversal filter configuration, where if the channel impulse has a peak value, the tap coefficients also have a peak value. The method that Gibson describes adjusts the timing until the main tap coefficient is located at the peak of the impulse response. However, this method inherits the traditional problems present in a linear equalizer, such as noise enhancement.
Expanding on noise enhancement in a linear equalizer, one can represent the input to a linear equalizer as       x    ⁢          (      n      )        =                    ∑        i            ⁢                        h          ⁢                      (            i            )                          ⁢                  a          ⁢                      (                          n              -              i                        )                                +                  N        ⁢                  (          n          )                    .      In the above equation, the first term includes inter-symbol interference (ISI) and the second term represents Gaussian noise. A linear equalizer removes ISI not Gaussian noise. A linear equalizer includes a number of taps, each tap time delayed relative to its neighbor, the taps measuring the input. The equalizer multiplies the output of the taps by coefficients and sums the resulting terms. Thus, the equalizer also multiplies the Gaussian noise term in the input by the same coefficients and sums the resulting terms to increase the noise power. Consequently, Noise power is proportional to the number of taps.
Therefore, there is a need for improved systems and methods for timing recovery. There is a need for timing recovery systems with reduced noise enhancement. There is also a need for timing recovery systems that coordinate equalization and timing recovery.